This file outlines the sampling strategy for Rwanda
The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location.
For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on the Rwandan “District” administrative unit. There are 30 districts in Rwanda. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions.
For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials will be surveyed at the federal level, while 140 officials will be surveyed at the district/sector level. For selection of officials at the district and sector level, we will employ a cluster sampling strategy, where 10 regional offices are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also select at random 10 school districts from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts seven officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and three randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.
Sampling Approach for Global Education Policy Dashboard
This document will provide an overview of the sampling strategy used in the Global Education Policy Dashboard (GEPD) surveys, as well as remaining questions. New data for the dashboard will be collected using three main instruments: a School Survey, an Expert Survey, and a Survey of Public Officials. More information pertaining to each can be found below. The goal of the Global Education Policy Dashboard is to provide summary information at the national level on a set of 35 indicators and to allow countries to track progress on those indicators over a short time frame (every 2 years). Specifically, we aim to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to disaggregate by urban/rural.
School Survey: The School Survey will collect data primarily on Practices (the quality of service delivery in schools), but also on some de facto Policy and school-level Politics indicators. It will consist of streamlined versions of existing instruments—including SDI and SABER SD on teachers, 4th grade students, and inputs/infrastructure, TEACH on pedagogical practice, GECDD on school readiness of young children, and DWMS on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of SDI, the number of items within each module is significantly lower. In each country, this survey will be administered in a nationally representative sample of 200 schools, selected through clustered random sampling. As currently envisioned, the School Survey will include 8 short modules. Expert Survey: The Expert Survey will collect information to feed into the policy indicators. This survey will be filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey will have 4 modules with each including approximately ten questions. Survey of Public Officials: The Survey of Public Officials will collect information about the capacity and orientation of the bureaucracy, as well as political factors affecting education outcomes. This survey will be a streamlined and education-focused version of the civil-servant surveys that the Bank’s Bureaucracy Lab has implemented recently in several countries, and the dashboard team is collaborating closely with DEC and Governance GP staff to develop this instrument. As currently envisioned, the survey will be administered to a random sample of about 200 staff serving in the central education ministry and district education offices. It will include questions about technical and leadership skills, work environment, stakeholder engagement, clientelism, and attitudes and behaviors.
In order to visit two schools per day, we clustered at the sector level choosing two schools per cluster. With a sample of 200 schools, this means that we had to allocate 100 PSUs. We combined this clustering with stratification by district and by the urban rural status of the schools. The number of PSUs allocated to each stratum is proportionate to the number of schools in each stratum (i.e. the district X urban/rural status combination). The table below shows the breakdown of the number of selected schools by district and urban/rural status.
The table below shows the number of schools per sector and the identifying district. In most cases, two schools are sampled per district, but because we selected an initial list of 100 schools and then based on those 100 schools selected another 100 from among the same sectors, in a few cases we had 4 school selected in a sector. This was done because some sectors may have larger numbers of schools than others, and so forcing the number of schools in a sector to two doesn’t capture as well the heterogeneity in size among sectors.
We lack geocode information on schools at this sampling stage, but never the less we can still map our sampled schools to sectors and districts to see how they are allocated. The map below shows a heat map based on the number of schools per district.
## Joining, by = "district"## Warning: Column `district` joining character vector and factor, coercing
## into character vector## Loading required package: spThe following map is similar, but plots sectors with a sampled school.
## Joining, by = c("sector", "district")## Warning: Column `sector` joining character vector and factor, coercing into
## character vector## Warning: Column `district` joining character vector and factor, coercing
## into character vector